

from __future__ import annotations

import math


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			if depth < 0:
						raise ValueError("""Depth cannot be less than 0""")

			if not scores:
						raise ValueError("""Scores cannot be empty""")

			if depth == height:
						return scores[node_index]

			return (
			    max(
			        minimax(depth + 1  ,	node_index * 2  ,	a__  ,	a__  ,	a__)  ,	minimax(depth + 1  ,	node_index * 2 + 1  ,	a__  ,	a__  ,	a__)  ,	)
			    if is_max
			    else min(
			        minimax(depth + 1  ,	node_index * 2  ,	a__  ,	a__  ,	a__)  ,	minimax(depth + 1  ,	node_index * 2 + 1  ,	a__  ,	a__  ,	a__)  ,	)
			)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						List[Any] 							= [9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
			a_						:						Tuple 							= math.log(len(a__)  ,	2)
			print(f'''Optimal value : {minimax(0  ,	0  ,	a__  ,	a__  ,	a__)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


